In deep learning-based models, it is hard to ensure that the finally generated features are better than the intermediate features. ResNet [1]} improves the scenario by including identity mapping. It enables the researchers to build deeper network architectures that can learn the difference between the input and target. It ensures better accuracy in the results. Proceeding in this direction, we propose to include ResBlocks along with MLP and CNN layers. The final features of this model are rich in information that enables better segmentation results while performing not worse than the state-of-the-art classification results.
